A

add_case

add_column

add_row

as.tibble

as_data_frame

as_tibble

as_tibble_col

as_tibble_row

C

column_to_rownames

D

data_frame

data_frame_

deframe

E

enframe

F

frame_data

frame_matrix

G

glimpse

H

has_rownames

I

is.tibble

is_tibble

K

knit_print.trunc_mat

L

lst

lst_

N

new_tibble

R

remove_rownames

repair_names

rowid_to_column

rownames_to_column

S

set_tidy_names

T

tbl_sum

tibble

tibble_

tibble_row

tidy_names

tidy_names

is_syntactic

make_syntactic

append_pos

describe_tidying

tribble

trunc_mat

V

validate_tibble

view

Unexported functions

-

$.tbl_df</h3> <p><img src="data:image/png;base64,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" /></p> </div> <div id="tbl_df-1" class="section level3"> <h3>$<-.tbl_df

[.tbl_df

[[.tbl_df

[[<-.tbl_df

[<-.tbl_df

A

accumulate

accumulate

f

accumulate_right

accumulate_right

f

add_to_env

add_to_env2

args_recycle

args_recycle

1

as.data.frame.tbl_df

as_friendly_type

as_tibble.data.frame

as_tibble.default

as_tibble.list

as_tibble.matrix

as_tibble.matrix

.name_repair

as_tibble.NULL

as_tibble.poly

as_tibble.table

as_tibble.ts

as_tibble_deep

B

big_mark

bullets

C

cells_to_col_idx

check_all_lengths_one

check_minimal

check_minimal_names

check_names_before_after

check_names_before_after_character

check_needs_no_dim

check_valid_col

check_valid_cols

cnd_names_non_na

cnd_names_non_null

cnd_signal_if

coalesce2

col_lengths

collapse

commas

compact

compat_as_lazy

compat_as_lazy_dots

compat_lazy

compat_lazy_dots

compat_lazy_dots

1

compat_name_repair

D

detect

detect_dot_dot

detect_duplicates

detect_empty_names

detect_index

dim_desc

discard

E

ensure_full_stop

error_add_rows_to_grouped_df

error_already_has_rownames

error_as_tibble_row_bare

error_as_tibble_row_size_one

error_assign_columns_non_missing_only

error_assign_columns_non_na_only

error_assign_incompatible_size

error_assign_incompatible_type

error_assign_rows_non_na_only

error_both_before_after

error_column_names_cannot_be_dot_dot

error_column_names_cannot_be_empty

error_column_names_must_be_unique

error_column_scalar_type

error_dim_column_index

error_duplicate_column_subscript_for_assignment

error_duplicate_row_subscript_for_assignment

error_enframe_has_dim

error_enframe_value_null

error_frame_matrix_list

error_glimpse_infinite_width

error_incompatible_new_cols

error_incompatible_new_rows

error_incompatible_size

error_incompatible_size

1

error_na_column_index

error_names_must_be_non_null

error_names_must_have_length

error_need_rhs_vector

error_need_rhs_vector_or_null

error_new_columns_at_end_only

error_new_rows_at_end_only

error_new_tibble_must_be_list

error_new_tibble_needs_nrow

error_subset_columns_non_missing_only

error_subset_matrix_must_be_logical

error_subset_matrix_must_be_scalar

error_subset_matrix_must_have_same_dimensions

error_subset_matrix_scalar_type

error_tibble_row_size_one

error_tribble_c

error_tribble_lhs_column_syntax

error_tribble_needs_columns

error_tribble_non_rectangular

error_tribble_rhs_column_syntax

error_unknown_column_names

every

extract_frame_data_from_dots

extract_frame_names_from_dots

F

fast_nrow

fix_oob

fix_oob_invalid

fix_oob_negative

fix_oob_positive

foreign_caller_env

format.tbl

format.trunc_mat

format_body

format_comment

format_extra_vars

format_header

format_knitr_body

format_n

format_v

format_v.character

format_v.default

format_v.factor

format_v.list

friendly_type_of

G

glimpse.data.frame

glimpse.data.frame

1

glimpse.default

glimpse.tbl

glimpse.tbl

1

guess_nrow

H

has_null_names

has_tibble_arg

I

imap

index

invalid_df

is_rstudio

is_syntactic

is_syntactic_impl

is_tight_sequence_at_end

is_valid_col

J

justify

K

keep

L

limit_pos_range

lst_quos

lst_to_tibble

M

make_valid_col

map

map_chr

map_cpl

map_dbl

map_if

map_int

map_lgl

map_mold

map2

map2_chr

map2_cpl

map2_dbl

map2_int

map2_lgl

matrix_to_cells

matrix_to_cells

1

matrixToDataFrame

mult_sign

N

na_value

name_or_pos

names<-.tbl_df

nchar_width

needs_dim

needs_list_col

negate

negate

1

new_data_mask_with_data

P

pluck

pluck_chr

pluck_cpl

pluck_dbl

pluck_int

pluck_lgl

pluralise

pluralise_commas

pluralise_count

pluralise_msg

pluralise_n

pmap

pos_from_before_after

pos_from_before_after_names

pre_dots

probe

problems

Q

quote_n

quote_n.character

quote_n.default

R

raw_rownames

rbind_at

recycle_columns

reduce

reduce

f

reduce_right

reduce_right

f

register_if_pillar_hasnt

repaired_names

replace_if_pillar_has

result_vectbl_wrap_rhs

row.names<-.tbl_df

S

safe_match

safe_match_3_0

safe_match_default

same_as_tbl

same_as_tbl_code

same_as_tbl_code

1

scoped_lifecycle_errors

scoped_lifecycle_silence

scoped_lifecycle_warnings

set_default_name

set_dftbl_chunk_hook

set_dftbl_chunk_hook

dftbl_chunk_hook

set_dftbl_error_hook

set_dftbl_error_hook

dftbl_error_hook

set_dftbl_hooks

set_dftbl_knit_hook

set_dftbl_knit_hook

dftbl_knit_hook

set_dftbl_opts_hook

set_dftbl_opts_hook

dftbl_opts_hook

set_dftbl_source_hook

set_dftbl_source_hook

dftbl_source_hook_one

dftbl_source_hook

set_dftbl_warning_hook

set_dftbl_warning_hook

dftbl_warning_hook

set_repaired_names

set_tibble_class

set_tibble_subclass

shrink_mat

signal_superseded

size_sum

some

spaces_around

splice_dfs

splice_dfs

1

split_lines

str.tbl_df

str_trunc

string_to_indices

strwrap2

subclass_col_index_errors

subclass_col_index_errors

1

subclass_name_repair_errors

subclass_name_repair_errors

1

2

3

subclass_row_index_errors

subclass_row_index_errors

1

subclass_tribble_c_errors

subclass_tribble_c_errors

1

T

tbl_expand_to_nrow

tbl_subassign

tbl_subassign_col

tbl_subassign_matrix

tbl_subassign_matrix

1

tbl_subassign_row

tbl_subassign_row

1

tbl_subset_col

tbl_subset_matrix

tbl_subset_row

tbl_subset2

tbl_sum.default

tbl_sum.tbl

tibble_error

tibble_error_class

tibble_glimpse_width

tibble_opt

tibble_quos

tibble_width

tick

tick_if_needed

transpose

transpose

1

turn_frame_data_into_frame_matrix

turn_frame_data_into_tibble

turn_matrix_into_column_list

type_sum.tbl_df

U

update_tibble_attrs

use_repair

V

validate_nrow

validate_rectangular_shape

vecpurrr_index

vectbl_as_col_location

vectbl_as_col_location2

vectbl_as_col_subscript2

vectbl_as_new_col_index

vectbl_as_new_row_index

vectbl_as_row_index

vectbl_as_row_location

vectbl_as_row_location2

vectbl_recycle_rhs

vectbl_recycle_rhs

1

vectbl_recycle_rhs_names

vectbl_recycle_rows

vectbl_restore

vectbl_strip_names

vectbl_wrap_rhs_col

vectbl_wrap_rhs_row

W

walk

warn_text_se

warn_underscored

with_lifecycle_errors

with_lifecycle_silence

with_lifecycle_warnings

wrap